Google ACE Practice Question: Ensuring Successful Operation of a Cloud Solution
You have a Cloud Run service that is experiencing high latency. You want to analyze the latency distribution of requests. Which Google Cloud tool should you use?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Cloud Trace
Cloud Trace is a distributed tracing service that collects latency data from applications and provides detailed analysis, including latency distributions and per-request traces.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Debugger
Why it's wrong here
Cloud Debugger is a production debugging tool that lets you inspect the live state of your code—such as call stacks, local variables, and heap references—without stopping the instance. It is fundamentally state-inspection, not timing-instrumentation: you cannot get request latencies, span durations, or waterfall breakdowns from a breakpoint. High-latency diagnosis requires identifying where time is spent across services, which Debugger simply does not capture.
- ✗
Cloud Logging Log Explorer
Why it's wrong here
Cloud Logging Log Explorer is designed for filtering, querying, and viewing raw log entries—for example, request logs, application logs, or error messages—but each log entry is an independent event, not a correlated trace. While you might infer that a request was slow by looking at timestamps, Log Explorer does not reconstruct the full request path, show spans, or provide latency percentiles or trace waterfalls. It lacks the distributed-trace correlation needed to pinpoint the exact component causing high latency.
- ✓
Cloud Trace
Why this is correct
Cloud Trace is purpose-built for latency analysis. It collects latency data from Cloud Run and other GCP services, then generates distributed traces with spans that show the duration of each operation—such as receiving the request, calling downstream dependencies, and returning the response. Trace features like waterfall views, latency distributions, and per-trace breakdowns let you identify exactly which service or API call is the bottleneck, making it the correct tool for high-latency issues.
- ✗
Cloud Monitoring Metrics Explorer
Why it's wrong here
Cloud Monitoring Metrics Explorer is for viewing pre-aggregated time-series metrics—like request count, error count, or average latency—over time windows. It can tell you that latency is high on average, but it cannot show you the individual request lifecycle or which specific backend call within the request is slow. Metrics Explorer lacks trace spans and per-request detail, so it is unsuitable for root-causing the exact source of high latency on Cloud Run.
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Related to this question
Learn chapter
Google Cloud Platform Overview
Key term
Trace
A trace is a record of the path and timing of a request or operation as it moves through components in a system, used to monitor performance and troubleshoot issues.
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This ACE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the ACE exam.